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Journal of Computational Science 5 (2014) 99–105

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TOPSIS method for quality credit evaluation: A case of air-conditioning market inChina

Xiaoqian Zhua,b, Fei Wangc, Haiyan Wangd, Changzhi Lianga,b, Run Tangd, Xiaolei Suna, Jianping Lia,∗

a Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, Chinab University of Chinese Academy of Sciences, Beijing 100190, Chinac School of Engineering, University of Chinese Academy of Sciences, Beijing 100190, Chinad Jiangsu Province Institute of Quality & Safety Engineering, Nanjing 210046, China

a r t i c l e i n f o

Article history:Received 4 October 2012Received in revised form 30 January 2013Accepted 4 February 2013Available online 27 February 2013

Keywords:Quality creditTOPSISChinaAir-conditioning market

a b s t r a c t

Quality credit is a new concept invented in China and to the best of our knowledge, there hasn’t beena widely-accepted quality credit indicator system and no quantitative method has been employed inquality credit evaluation up to now. To take the researches on quality credit a step further, this paperaims to establish a quality credit evaluation indicator system for air-conditioning enterprises in Chinesemarket and use TOPSIS (technique for order preference by similarity to ideal solution) method to evaluatequality credit of the enterprises. Based on the data of 8 air-conditioning enterprises, including 6 Chineseenterprises and 2 Japanese enterprises, three experiments with three different indicator systems are usedto determine the final indicator system and verify the feasibility and effectiveness of TOPSIS. In Experi-ment one, an original indicator system is established to evaluate the quality credit of the 8 enterprises. InExperiment two and three, two reasonably adjusted indicator systems are used and the indicator systemin Experiment three is the final one that we recommend. The analysis of experiments verifies that theproposed quality credit indicator system is reliable and TOPSIS is suitable for quality credit evaluation.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

In recent years Chinese food enterprises have experiencedunprecedented quality credit crisis. Some enterprises collapseinstantly owing to their terrible quality credit while others strug-gle to survive. Quality credit is a new concept invented in China in2002 by Chinese central government. Afterwards, several organiza-tions such as China National Institute of Standardization and ChinaProduct Quality Association have devoted energy to studying qual-ity credit. Two Chinese government documents on quality credithave been issued in 2006 and 2009 up to now [1,2], in which thedefinition of quality credit and some relative terms are proposedinitially.

Quality credit is an important component of enterprise credit,that is, quality credit reflects the enterprise’s credit in the field ofproduct quality. The enterprise credit comprehensively reflects theoverall credit of enterprises. The credit between the enterprise andthe bank, the credit between the enterprises and other enterprises,the credit between the enterprise and the government, the creditbetween the enterprises and the consumer and so on are all thecomponents of enterprise credit. The credit between the enterprise

∗ Corresponding author. Tel.: +86 10 59358805.E-mail address: ljp@casipm.ac.cn (J. Li).

and the consumer is the quality credit reflecting whether the enter-prise offers products or services to the consumer as promissoryquality.

Quality credit evaluation result, such as credit score or rank, isan objective and concise reference for the consumer, governmentand enterprise in decision making. For consumers, quality creditscore can be used as a reference when they want to buy productsfrom enterprises. Consumers are faced with a large amount of alter-native products all the time and how to choose a worthy productis a question. If the quality credit score result is accessible to theconsumer, they can easily choose the product from the enterprisewith high quality credit score. For government, the quality creditscores are the reference for supervision. The enterprises with lowquality credit score should be in the key regulatory target list. Onthe contrary, the government could reward the high quality creditenterprises so as to encourage them. For enterprises, the qualitycredit score can tell themselves whether their quality credits aregood enough. An interpretable credit scoring method can also tellthem which aspects they should improve to promote their qualitycredits.

Although the concept of quality credit and its evaluation resultsare very important, the researches on quality credit are still in avery preliminary stage. To the best of our knowledge, there is norelevant English research paper on quality credit. Even the num-ber of Chinese researches is very small. Those Chinese papers and

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100 X. Zhu et al. / Journal of Computational Science 5 (2014) 99–105

Chinese government documents focus on discussing the definitionand indicator system of quality credit. The proposed indicator sys-tems in those Chinese papers are very sophisticated and differentfrom each other. Up to now, there is no widely-accepted indicatorsystem for quality credit and no quantitative method is employedto quality credit evaluation.

The first objective of this study is to put forward a concise qualitycredit evaluation indicator system for air-conditioning enterprisesin Chinese market. TOPSIS is an interpretable evaluation methodwhich can give each evaluated object a specific score. So the sec-ond objective of this paper is to use TOPSIS method to evaluatethe quality credit of the enterprises. The experiment results verifythat the proposed indicator system is reliable and using TOPSIS toevaluate quality credit is feasible and effective.

The rest of this study is organized as follows. Section 2 discussesthe definition and evaluation of quality credit. Section 3 introducesthe TOPSIS method. Section 4 presents a detailed case of qualitycredit evaluation for air-conditioning enterprises in Chinese mar-ket. Section 5 provides conclusions and possible future directions.

2. Quality credit definition and evaluation

Quality credit is first proposed to describe enterprise’s capacityand performance of complying with laws as well as the commit-ment to its product quality by the General Administration of QualitySupervision, Inspection and Quarantine (GAQSIQ) of the People’sRepublic of China [1]. In 2009 the GAQSIQ and StandardizationAdministration of People’s Republic of China (SAC) jointly release anational standard in which the quality credit is defined as the capac-ity of the enterprise to obtain and maintain the credit of quality[2].

The former definition is preferred in this paper, as capacitydepicts an enterprise’s potential ability to manage quality creditwhile performance represents the result it has achieved. Moreover,the willingness to fulfil the promissory quality credit is also coveredin this definition. So quality credit in this paper refers to the willing-ness, capacity and actual performance of an enterprise to maintainhigh product quality. Some researches mistake product for the sub-ject of the quality credit, however, it is the enterprise that owns thequality credit rather than the product, in other words, the subjectof quality credit is the enterprises. The quality credit can somehowbe perceived as the credit of enterprises in terms of product quality.

As the prerequisite of quality credit evaluation, the indicatorsystem should essentially represent the characteristics of qualitycredit. Previous works emphasize on the indicator selection andclassification. Sophisticated indicators have been selected aimingto extensively cover every aspect [3]. However some redundantindicators merely increase the time and effort to evaluate qual-ity credit and offer no help to the improvement of validity. In thispaper, to make the indicatory system effective, some improper indi-cators are abandoned and some helpful indicators are added. Basedon the definition of quality credit, evaluation indicators are catego-rized into three aspects, namely the willingness to maintain qualitycredit, capacity of the enterprise and practical performance. It isnoteworthy that the indicators of different industries should varyas a result of different product characteristics and operation pat-terns. In the following text, a general indicator system is proposedand it can be adjusted slightly according to the particularity of aspecific industry.

For the willingness to maintain quality credit, the indicators canbe summarized into the company, law and encouragement layers.Firstly, the company layer describes the willingness of preservingtangible and intangible assets. For instance, the value of a brand isaccumulated by years’ efforts and ignorance of the quality creditwill lead to business scandal that makes these efforts in vain.

Secondly, the willingness is influenced by laws and regulations.For example, the severer the punishment is, the better willingnessthe enterprises will have to obey the laws on quality credit. Finally,the enterprise will be more enthusiastic to preserve its qualitycredit with encouragement. For example, quality awards willencourage the enterprise to improve its quality credit.

Capacity of the enterprise implies the potential ability to meetthe quality commitment. The capacity is illustrated by the man-ufacturing and financial layers. The manufacturing layer of anenterprise includes the research investment, technical ability, qual-ity certification and adopted standards, which will influence themanufacturing management and eventually product quality fromdifferent dimensions. The financial layer is analogous to part ofenterprise credit which is mainly concerned with financial affairs.The profit rate, sales growth and market shares are the commonindicators to depict enterprise capacity.

As for the practical performance, the indicators are summa-rized as the law and customer layers. Violation of laws will arousesuspicion about the product quality. For example, illegal recordsand accidents caused by terrible product quality can devastate anenterprise’s quality credit. From the perspective of customers, thereliability of the propaganda and customer’s satisfaction degreeafter using the product can reflect the quality credit.

3. TOPSIS for quality credit evaluation

Credit evaluation is a hotspot in researches. Many mathematicalmodels, such as support vector machine (SVM), modified SVM [4,5],decision tree (DT), linear discriminant analysis (LDA), quadratic dis-criminant analysis (QDA), logistic regression (log R) and k-nearestneighbour classifier (k-NN), have been applied to credit evaluationso far [6]. Generally speaking, accuracy, complexity and inter-pretability are accepted as three major criterions in choosing creditevaluation methods [7]. Accuracy is the basic and decisive criterionbecause the method with unreliable evaluation results is use-less. Complexity is important because a time-consuming methodentails higher hardware as well as time cost. Besides accuracy andcomplexity, interpretability is always taken into consideration inpractice because the evaluation process and results should be easyto understand and explain. Nonetheless, few above-mentionedpopular methods can perform well in accuracy, complexity andinterpretability simultaneously. For example, SVM performs rel-atively well in accuracy but the parameters selection process istime-consuming and the model is a black box that is not inter-pretable [8]. The classification process of the DT is very clear andits computational efficiency is relatively high and yet its accuracyis not satisfactory [9]. Modified SVM, such as LSSVM, performs bet-ter in accuracy and complexity, however, the interpretability hasn’tbeen improved [10]. The computing speed of LDA, QDA, LogR andk-NN is relatively fast, yet their accuracies are just so-so and theyare also not interpretable [7,9].

Technique for order preference by similarity to ideal solution,TOPSIS for short and first developed by Hwang and Yoon [11], isone of the classical multi-criteria decision-making (MCDM) meth-ods known for reliable evaluation results, quick computing processand ease of use and understanding [12]. Moreover, it can give aspecific score to the every evaluated target rather than just givesclassification result. TOPSIS is widely used in all kinds of evalua-tion studies, but not yet in quality credit evaluation. In this study,TOPSIS is used to evaluate quality credit.

TOPSIS is based on the concept that the most preferred alter-native should have the shortest distance from the positive idealsolution (PIS) and the largest distance from the negative ideal solu-tion (NIS). The PIS refers to a solution that maximizes the benefitcriteria and minimizes the cost criteria, whereas the NIS is the

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opposite that minimizes the benefit criteria and maximizes thecost criteria. The TOPSIS method considers the distance to boththe PIS and the NIS, simultaneously [13]. The quality credit of anenterprise is reflected by indicators listed in Section 2. A high qual-ity credit enterprise should have better indicator values than thelow quality credit enterprise. So the ideal quality credit enterprisewith the best indicator values and the worst enterprise with theworst indicator values can be the benchmarks for evaluating qual-ity credit. The quality credit of an enterprise can be represented asthe distance of the enterprise from ideal enterprise and worst enter-prise. The closer the enterprise to ideal enterprise and the fartherto the worst enterprise, the higher the quality credit score is, andvice versa. Some indicators, so-called benefit indicator, affect thequality credit positively while other indicators, so-called cost indi-cator, affect the quality credit negatively. The ideal quality creditenterprise should have the largest benefit indicator values and thesmallest cost indicator values. The worst quality credit enterpriseis just the reverse. The quality credit scoring process using TOPSISis as follows [14,15].

Assume there are n enterprises to be evaluated and each enter-prise has m evaluation indicators. Let X = [xij]nxm denotes decisionmatrix, where xij is the jth indicator value of the ith enterprise. Letwj denotes the weight of the jth indicator.

Step 1: Normalize the decision matrix

rij = xij√∑ni=1x2

ij

, i = 1, · · ·, n, j = 1, · · ·, m (1)

where rij denotes the normalized value of jth indicator of the ithenterprise.

Step 2: Weight the normalized decision matrix

vij = wjrij, i = 1, · · ·, n, j = 1, · · ·, m (2)

where vij denotes the weighted normalized value of jth indicator ofthe ith enterprise. Weights are always set subjectively by expertsor regulators.

Step 3: Determine PIS and NIS

A+ ={

v+j

, j = 1· · ·, m}

A− ={

v−j

, j = 1· · ·, m} (3)

where A+ denotes the PIS and A− denotes the NIS. If jth indica-tor is benefit indicator, vj

+ = max{vij, i = 1,. . .,n} and vj− = min{vij,

i = 1,. . .,n}. On the contrary, if jth indicator is cost indicator,vj

+ = min{vij, i = 1,. . .,n} and vj− = max{vij, i = 1,. . .,n}. PIS and NIS are

the weighted normalized best and worst enterprises. Meanwhile,it is notable that experts or regulators can also determine what thebest and the worst quality credit enterprises are according to theiropinions and needs.

Step 4: Calculate the distance from each enterprise to PIS andNIS

S+i

=√∑m

j=1(vij − v+j

)2, i = 1, · · ·, n

S−i

=√∑m

j=1(vij − v−j

)2, i = 1, · · ·, n

(4)

where Si+ denotes the distance between the ith enterprise and the

PIS, and Si− denotes the distance between the ith enterprise and

the NIS.Step 5: Calculate the quality credit score

Ci = S−i

S−i

+ S+i

, i = 1, · · ·, n (5)

where Ci is called quality credit score in this study which representsthe degree of closeness of the ith enterprise to PIS and NIS.

Fig. 1. Quality credit rating results with all evaluation indicators.

4. Experiment

4.1. Experiment design

In this experiment, TOPSIS is applied to evaluate the qualitycredit of 8 air-conditioning enterprises in Chinese market. Threeexperiments are conducted in this section. In Experiment one, allenterprises are evaluated based on the initially proposed indica-tor system. However, the evaluation results seem irrational. So inExperiment two, an indicator, sales growth, are removed and allthe enterprises are evaluated based on the remaining indicators.Moreover, in the remaining indicators, it is noteworthy that twoindicators may not fair for Japanese enterprises. So in Experimentthree, the particularity of the two indicators is taken into consid-eration. Finally, the quality credit evaluation indicator system inExperiment three is believed to be a relatively concise and reli-able evaluation indicator system for air-conditioning enterprisesin Chinese market.

Besides, three points need to be made clear. (1) Weights arealways set by experts for regulators. In this study, in order to sim-plify this experiment, equal weights are set to all indicators, thusStep 2 in Section 3 can be skipped. (2) The quality credit scoresare between 0 and 1, for the ease of understanding, the final scoreshown in the Tables 3–5, Figs. 1–3 are the scores multiplied by100 and rounded. (3) After the quality credit score is assigned, inorder to make the readers more sensitive to the changes of rank-ing in different experiments, we classify the 8 enterprises into 3grades, i.e. Grade A, Grade B and Grade C. Grade A contains qualitycredit enterprises with top 3 quality credit scores. Grade C contains2 enterprises with the last and second last quality credit scores.Grade B contains the remaining 3 enterprises. Therefore, Grade A

Fig. 2. Quality credit rating results after removing indicator SG.

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Table 1Quality credit evaluation indicators of air-conditioning enterprises.

Evaluation aspect Specific indicator Type Detail

Willingness Chinese well-knowntrademark (CWT)

Numerical How many years the trademark of theenterprise has become Chinese Well-knownTrademark

Rewards amount (RA) Numerical How many China national rewards theenterprise has attained

Capacity Research and developmentratio (R&D)

Numerical R&D investment/sales

ISO9000 systemcertification (ISC)

Numerical How many years the enterprise has attainedISO9000 system certification

Sales margins (SM) Numerical Gross profit/salesSales growth (SG) Numerical (Sales of this year-sales of last year)/sales of

last yearMarket share (MS) Numerical What percentage the sales of the enterprise

accounts for the total sales of the top 10enterprises

Performance Customer satisfaction (CS) Numerical How much degree the product meets thecustomers’ needs and expectations

Table 2Quality credit evaluation data of 8 enterprises in China air-conditioning market.

ID Enterprise CWT (year) RA R&D (%) ISC (year) SM (%) SG (%) MS (%) CS

1 C1 12 8 5.44 15 22.54 43.78 29.75 75.72 C2 12 8 4 16 17.69 50.63 28.90 74.93 C3 20 6 3.95 17 17.79 31.21 13.06 74.84 J1 0 1 3.59 12 33.86 −17.55 3.75 74.55 C4 12 5 5 17 11.67 46.20 5.80 73.06 J2 5 1 6.40 18 5.82 −6.60 2.42 71.17 C5 2 4 1.26 12 19.50 38 5.68 69.18 C6 7 1 1.15 14 19.27 80 6.89 66.7

stands for the good quality credit grade, Grade B stands for the mid-dle quality credit grade and Grade C stands for the relatively badquality credit grade.

4.2. Evaluation indicators

The quality credit evaluation system described in Section 2 is arelatively comprehensive one. However, in this empirical section,the quality credit evaluation system is slightly adjusted accord-ing to the characteristics of air-conditioning industry and dataavailability. Three aspects are considered in evaluating the qual-ity credit of air-conditioning enterprises, i.e. willingness, capacityand performance. The three aspects are measured by 8 spe-cific indicators that are shown in Table 1. All indicators arebeneficial.

Fig. 3. Quality credit rating results after removing indicator SG and considering theparticularity of CWT and RA.

4.3. Data description

According to customer satisfaction, 10 top enterprises that con-sist of 8 Chinese enterprises and 2 Japanese enterprises are chosenin this experiment. Two Chinese enterprises are finally abandonedowing to the lack of data. The name of the remaining 8 enterprisesare replaced by C1, C2, C3, C4, C5, C6 for Chinese enterprises andJ1, J2 for Japanese enterprises for the sake of data confidentiality.

The data of the 8 enterprises is shown in Table 2. All indicatornames in Table 2 are simplified to acronyms. Among these indica-tors, CWT is attained from Chinese Well-Known Mark website [16].RA, R&D, ISC, SM and SG are attained from annual reports 2010 andofficial websites of these enterprises. MS and CS are attained fromChinese annual air-conditioning market report 2010.

4.4. Evaluation result

4.4.1. Experiment oneBy using TOPSIS method described in Section 3, the quality credit

scores of the 8 enterprises are calculated based on all the indica-tors in Table 1. Table 3 shows the quality credit score and rankof the enterprises. The larger the credit score is, the better the

Table 3Quality credit evaluation results with all evaluation indicators.

ID Enterprise Quality credit score Rank

1 C1 71 12 C2 68 23 C3 59 34 J1 29 75 C4 50 46 J2 28 87 C5 35 68 C6 47 5

Note: Enterprises are ranked according to quality credit score, similarity hereinafter.

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Table 4Quality credit evaluation results after removing indicator SG.

ID Enterprise Quality credit score Rank

1 C1 74 12 C2 68 23 C3 62 34 J1 34 55 C4 45 46 J2 32 67 C5 25 88 C6 26 7

quality credit of the enterprise is expected to be, and vice versa.It is observed that the quality credit scores of all 6 Chinese enter-prises are larger than the 2 Japanese enterprises. Specifically, thequality credit scores of C1, C2, C3, C4, C5 and C6 are 71, 68, 59, 50,35 and 47 respectively, ranking among the top 6. The quality creditscores of J1 and J2 are 29 and 28, respectively, ranking the last andsecond last.

In order to visualize the grade results, Fig. 1 is given. Eight enter-prises are classified into 3 grades according to their quality creditscores in Table 3. Grade A contains 3 Chinese enterprises with high-est, second highest and third highest quality credit scores, i.e. C1,C2 and C3. Grade B also contains 3 Chinese enterprises, i.e. C4, C6and C5. The last grade, Grade C, contains the 2 Japanese enterprises,i.e. J1 and J2.

4.4.2. Experiment twoJapanese appliance enterprises are famous for high technology

and high product quality in the world, so it is surprising that the2 Japanese enterprises are rated as the last grade, Grade C. To lookTable 2 carefully, it is easy to find out that the indicator SG may leadto this intuitively irrational evaluation results. The sales growths ofthe 6 Chinese enterprises are all above 30%, however, that if J1 andJ2 are −17.55% and −6.6%, respectively. So, in this experiment, weremove SG from the evaluation indicators. The enterprises are re-evaluated by TOPSIS with the remaining 7 indicators. The qualitycredit evaluation results after removing indicator SG are shown inTable 4.

Table 4 shows that after removing indicator SG, the quality creditscores of J1 and J2 improve. It is observed that the top 3 enter-prises are also C1, C2 and C3 with quality credit scores of 74, 68and 62. However, scores of 2 Chinese enterprises, C5 and C6, arelower than the 2 Japanese enterprises, J1 and J2, with scores of 34and 32, respectively.

Fig. 2 shows the grading results after removing indicator SG. It isobserved that J1 and J2 are in Graded B now. The last Grade, GradeC, contains 2 Chinese enterprises C6 and C5. The top 3 enterprisesin Grade A are also C1, C2 and C3.

4.4.3. Experiment threeThe credit score of J1 and J2 improve after removing the indica-

tor SG. Moreover, it is noteworthy that in the remaining indicators,CWT and RA may not seem fair for Japanese enterprises. Chi-nese enterprises may win China national rewards more easily thanJapanese enterprises. Likewise, the application of Chinese Well-known Trademark may be also easier for Chinese enterprises. Soin this experiment, to take the particularity of CWA and RA intoconsideration, Japanese enterprises are evaluated based on theremaining 5 indicators. Specifically, Chinese enterprises are eval-uated based on 7 indicators (CWT, RA, R&D, ISC, SM, MS and CS)while Japanese enterprises are evaluated based on the 5 indicators(R&D, ISC, SM, MS and CS). So in the evaluation process, the CWTand RA value of the best and worst enterprises are determined inthe light of Chinese enterprises. That is, the non-normalized bestand worst enterprises data are (20, 8, 6.4, 18, 33.86, 29.75, 75.7) and

Table 5Quality credit evaluation results after removing indicators SG and considering theparticularity of CWT and RA.

ID Enterprise Quality credit score Rank

1 C1 73 12 C2 67 23 C3 61 34 J1 46 45 C4 43 56 J2 37 67 C5 25 78 C6 24 8

(2, 1, 1.15, 12, 5.82, 2.42, 66.7) for Chinese enterprises and are (6.4,18, 33.86, 29.75, 75.7) and (1.15, 12, 5.82, 2.42, 66.7) for Japaneseenterprises. The quality credit evaluation results of this experimentare shown in Table 5.

Table 5 shows that the credit score and rank of J1 improve.Specifically, the score of J1 is lower than C4 in Experiment two butJ1 surpass C4 in this experiment. In addition, J2 are still ranks 6th,C1, C2 and C3 are still among the top 3 and C5 and C6 are still thelast and second last.

Fig. 3 shows the grading results of Experiment three. Grade Balso contains J1, C4 and J2, however, J1 surpass C4 in this exper-iment. This proves that CWA and RA can affect the quality creditevaluation of Japanese enterprises. Grade A also contains C1, C2, C3and Grade C also contains C5 and C6.

After Experiment three, a simple and reliable quality credit eval-uation system is established. The evaluation indicator system inExperiment three is believed to be the most reliable one in thisstudy.

4.5. Results analysis

4.5.1. The reliability of the evaluation indicator systemThough the 3 experiments, a simple and reliable quality credit

evaluation system for air-conditioning enterprises in Chinese mar-ket is established. The evaluation indicator system in Experimentthree is the final and most reliable one. The reliability of the evalu-ation indicator system in Experiment three is discussed from threeaspects as follows.

Firstly, removing SG from the indicator system is reasonable.In Experiment one, the Japanese enterprises are scored the lowest,which is intuitively wrong. By checking the data in Table 2, it iseasy to find out that sales growth may be responsible for it. Thesales growth of the 6 Chinese enterprises is all above 30% in 2010,however, that of J1 and J2 are respectively −17.55% and −6.6%.It generally holds that sales growth should be considered in theevaluation of quality credit because sales growth reflects the finan-cial capability of an enterprise. However, in this case, sales growthshould not be included. As a matter of fact, on one hand, salesgrowth of air conditioner is significantly affected by the policy ofhome appliance subsides for rural areas in China while Japanese airconditioners are not on the subsidy list. On the other hand, Chinais not the major market of the 2 Japanese enterprises despite thefact that they have large market share in the world. According to theChina air-conditioner market analysis annual report 2010, the mar-ket share of foreign enterprises in 2010 has drastically decreased tohalf of that in 2005, from which we can conclude that the marketshare of the 2 Japanese enterprises are shrinking. Their market-ing strategy is to sell less but high priced air conditioners in thecompetition with Chinese enterprises to make more profits [17]. Soadding sales growth into indicator system is not helpful for eval-uating quality credit. On the contrary, it may even confound theevaluation.

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Secondly, considering the particularity of CWT and RA is nec-essary. CWT and RA are always included in quality credit indicatorsystem because it is considered that they can reflect the enterprise’swillingness to maintain quality credit. The longer the trademarkhas become Chinese well-known trademark, the higher the brandvalue is, so the enterprises are more motivated to maintain qual-ity credit. The effects of China national rewards on enterprises arejust like this. Awards will encourage the enterprises to improve itsquality credit. However, when the evaluated enterprises containforeign enterprises, the two indicators are not fair. It is more easilyfor Chinese enterprises to win Chinese well-known trademark andChina national rewards than foreign enterprises. So the best solu-tion is to include CWA and RA in Chinese enterprises evaluationand remove them in foreign enterprises evaluation.

Thirdly, the evaluation results are reliable. Fig. 3 shows thatGrade A includes 3 good quality credit enterprises, which are allChinese enterprises. Grade C includes 2 bad enterprises, which arealso Chinese enterprises. The remaining 1 Chinese enterprise andthe 2 Japanese enterprises are included in the Grade B. Accordingto the Chinese air-condition market analysis annual report 2010published by State Information Center of China, 3 famous Chineseenterprises maintain the lead in Chinese market which exactlyaccord with the enterprises in Grade A in this paper. The brandsof the three enterprises are also the only 3 appliance brands ratedas the top 50 most valuable Chinese brands in 2012 [18]. Last butnot the least, the top 6 enterprises in Grade A and Grade B are ratedas the global TOP 31 air-conditioning enterprises in 2010 [19].

From the above analysis, we can see that removing SG from theoriginal indicator system and considering the particularity of CWAand RA are necessary and reasonable. Moreover, the evaluationresults of the Experiment three are reliable. So it is verified that thefinal indicator system in Experiment three are feasible and effec-tive. Above analysis also reveals that indicators are closely related toindustry environment, product characteristic, national policy andso on, so the selection of proper indicators is very important in qual-ity credit evaluation. More studies on it are urgently needed in thefuture.

4.5.2. The applicability of TOPSIS methodTOPSIS is a suitable quality credit evaluation method on account

of several notable advantages. The first advantage is the evaluationresults of TOPSIS are robust. Three experiments are conducted inSection 4. In the three experiments, C1, C2 and C3 are always inthe Grade A. After removing sales growth, C5 and C6 are alwaysin Grade C. This indicates that the change of some indicators mayaffect the evaluation results of the specific enterprises but otherenterprises are not affected. TOPSIS is famous for the fewest rankreverse which is reflected as the robustness of evaluation results inthis study.

The second advantage is that the evaluation results are easyto understand and explain and so advices can be easily given tothe evaluated enterprises. The comparison of each indicator valuebetween the evaluated enterprises and the ideal enterprise orbetween high score enterprise and low score enterprise does makesome sense. According the comparison, low score enterprise canrealize which indicators they should improve. Take the results inTable 5 as an example. C1 gets the highest score because it out-performs all other enterprises. It performs the best in 3 of the 7evaluation indicators and the rest indicators are also close to thebest. Besides, C6 attain the lowest score with almost all of theirindicators the worst, reflecting that some problems may exist thequality credit of the enterprises. C6 are supposed to invest morefund into research and development, make effort to acquire highquality certification, promote its sales margins, market share andso on, so as to improve its quality credit. Other enterprises can alsobe analysed in the same way.

The third advantage is that it is easy to consider the experts’advice in TOPSIS. A cogent enterprise quality credit scoring is acomprehensive evaluation process that should consider both objec-tively model result and subjectively expert advice. TOPSIS caneasily combine expert opinion in the following three aspects. First,experts can set weights for each evaluation indicators accordingto their significance. Second, experts or regulators can set what anideal high quality credit enterprise is, that is, set the most preferredindicator values. Third, experts or regulators can set thresholds forquality credit score to attain credit rating result. For example, for anpercentile score, regulators can set 80 and 60 as thresholds accord-ing to their opinions, so enterprises with credit score greater than80 are classified as Grade A, enterprise with credit score greaterthan 60 while less than 80 are classified as Grade B, enterprise withcredit score lower than 60 are classified as Grade C.

5. Conclusion

Quality credit is a new concept which emerges in China andthere are only a few researches on it so far. In this study, a qualitycredit indicator system is proposed for air-conditioning enterprisesin Chinese market and TOPSIS method is employed to evaluatequality credit of the enterprises. The data of 8 enterprises, includ-ing 6 Chinese enterprises and 2 Japanese enterprises, is used in theexperiments. The experiment results show that the proposed indi-cator system is reliable. Also, it is verified that TOPSIS is suitablefor quality credit evaluation for three notable advantages. The firstadvantage is the evaluation results of TOPSIS are robust. The sec-ond advantage is that the evaluation results are easy to understandand explain. The third advantage is that it is easy to consider theexperts’ advice in TOPSIS. By using the indicator system and TOP-SIS method, every enterprise can attain a quality credit score whichgives a clear view of the quality credit condition of the enterprise.The larger the credit score is, the better the quality credit of theenterprise is expected to be, and vice versa. The quality credit scorecan be used as an important reference for the consumer, enterpriseand government in decision making.

Some further studies are also needed. The results analysis inthis paper reveals that quality credit evaluation indicators signif-icantly affect the soundness of evaluation results, so the selectionof proper indicators is very important and needs more studies.Besides, more mathematical models can be applied in quality creditevaluation. Finally, indicator systems of other industries also needsome researches. The results of those ongoing research works willbe reported in the near future.

Acknowledgements

This research is partially supported by National Science Foun-dation of China (No. 71071148), the Youth Innovation PromotionAssociation of Chinese Academy of Sciences and construction of thescience and technology infrastructure plan supported by Depart-ment of Science and Technology of Jiangsu Province.

References

[1] Opinions On Strengthening the Supervision of Corporate Credit Quality, Gen-eral Administration of Quality Supervision, Inspection and Quarantine of thePeople’s Republic of China, 2006 (in Chinese).

[2] China Quality Supervision, Summary of “General principles on enterprise qual-ity credit rating”, 2009 (in Chinese).

[3] Jiangsu quality and technology supervisory authority, Quality creditevaluation pilot schemet of industrial enterprises in Jiangsu province,http://www.jsqts.gov.cn/zjxx/InfoDetail/?InfoID=bc60c0e3-0f2d-4a6a-8d25-f708b1834162 (in Chinese).

[4] L. Wei, Z. Chen, J. Li, Evolution strategies based adaptive Lp LS-SVM, InformationSciences 14 (2011) 3000–3016.

Author's personal copy

X. Zhu et al. / Journal of Computational Science 5 (2014) 99–105 105

[5] J. Li, Z. Chen, L. Wei, W. Xu, G. Kou, Feather selection via least squares supportfeature machine, International Journal of Information Technology & DecisionMaking 4 (2007) 671–686.

[6] J.N. Crook, D.B. Edelman, L.C. Thomas, Recent develpments in consumercredit risk assessment, European Journal of Operational Research 183 (2007)1447–1465.

[7] J. Li, L. Wei, G. Li, W. Xu, An evolution-strategy based multiple kernels multi-criteria programming approach: the case of credit decision making, DecisionSupport System 2 (2011) 292–298.

[8] M. Doumpos, C. Zopounidis, V. Golfinopoulou, Additive support vectormachines for pattern classification, IEEE Transactions on Systems, Man, andCybernetics, Part B: Cybernetics 37 (3) (2007) 540–550.

[9] G. Wang, J. Ma, L. Huang, K. Xu, Two credit scoring models based on dual strategyensemble trees, Knowledge-Based Systems 26 (2012) 61–68.

[10] K.K. Lai, L. Yu, L. Zhou, S. Wang, Credit risk evaluation with least square supportvector machine, Lecture Notes in Computer Science 4062 (2006) 490–495.

[11] C.L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and Appli-cations: A State-of-the-Art Survey, Springer-Verlag, Berlin and New York, 1981.

[12] M.Y.T.I.C. Yurdakul, Development of a quick credibility scoring decision sup-port system using fuzzy TOPSIS, Expert Systems with Application 37 (2010)567–574.

[13] D. Wu, A TOPSIS data mining demonstration and application to credit scoring,International Journal of Data Warehousing &Mining 3 (2006) 1–10.

[14] H. Li, H. Adeli, J. Sun, J.G. Han, Hybridizing principles of TOPSIS with case-basedreasoning for business failure prediction, Computers & Operations Research 38(2011) 409–419.

[15] N.Y. Secme, A. Bayrakdaroglu, C. Kahraman, Fuzzy performance evaluation inTurkish banking sector using Analytic Hierarchy Process and TOPSIS, ExpertSystems with Applications 36 (2009) 11699–11709.

[16] Chinese Well-Known Mark website. http://www.21sb.com (accessed01.01.12).

[17] Xinhua News Agency, http://news.xinhuanet.com/fortune/2011-05/24/c 121450434.htm

[18] Millward Brown, Most Valuable Chinese Brands TOP 50, http://www.millwardbrown.com/BrandZ/BrandZ Top50 Chinese Brands.aspx

[19] Global Industry Analysts Inc, Air Conditioning Systems: Market ResearchReport, Market Publishers, 2011.

Xiaoqian Zhu received her B.S. in Information Manage-ment and System in July 2010 from University of Scienceand Technology of China. She is now a Ph.D. candidate inUniversity of Chinese Academy of Sciences. Her major isManagement Science and Engineering and her interestsfocus on credit scoring and bank risk management.

Fei Wang received his B.S. in logistics engineering fromBeijing University of Posts and Telecommunications,China in July 2010. He is the candidate of M.S. in logisticsengineering in University of Chinese Academy of Sciencesand is expected to graduate by July 2013. His researchinterests involve in credit evaluation, oil import portfolio,data mining, intelligent algorithms and decision supportsystem.

Haiyang Wang is a professor, executive dean of JiangsuProvince Institute of Quality & Safety Engineering. She gotdoctor degree of Engineering in Management Science andEngineering from Nanjing University of Science (China)in 2003.Now she also act as dean, master instructor ofdepartment of management science and engineering inNanJing University of Finance. Her research interests coverquality management, traffic economy, quality standard,strategic management, industry technical standard.

Changzhi Liang is a graduate student in the Universityof Chinese Academy of Sciences. He received his B.S. inPhysics at Peking University. His research interests focuson integration of financial risks within commercial banksand systemic risk on the interbank market. He also partic-ipated in the funded project ‘The Strategy and Planning ofEnergy Development for Qinghai Province’.

Run Tang is an assistant researcher of Jiangsu ProvinceInstitute of Quality & Safety Engineering. He has a PHDin management science. And he also is lecturer of Nan-jing University of Economics and Finance. His researchinterests include quality management, supply chain man-agement.

Xiaolei Sun is an assistant professor in the Institute of Pol-icy and Management, Chinese Academy of Sciences. Shehas a PhD in the University of Chinese Academy of Sci-ences. She has worked in two projects of National ScienceFoundation of China. Her research interests focus on therisk management, especially on the credit risk.

Jianping Li received the Ph.D. degree in Management Sci-ences and Engineering from University of Science andTechnology of China, Hefei in 2004, and currently is aprofessor in Institute of Policy and Management, Chi-nese Academy of Sciences. Prof. Li is the secretary andexecutive committee member of International Academyof Information Technology and Quantitative Management(IAITDM). His research interests include credit risk assess-ment, operational risk modelling, country risk modellingand risk integration modelling.